One challenge that presents itself when multiple people gather together to watch a video, for example, is to pick programming that everyone will enjoy. For example, a child may be interested in certain types of programming that may not appeal to an adult, and vice versa. Media companies have recommendation engines that can provide media users with suggestions for programming based on account usage (a static hybrid identity) or on individual user usage, which might be based on how an individual user has intentionally rated past content. However, the drawback with this approach is that such recommendation engines often make recommendations based on a solo media consumer or on most recent viewing history, rather than a longer sample set, which may result in an unsatisfactory experience for others who are also viewing the media.